Palestra Veículos Autônomos - Prof. Jay Farrell (University of California Riverside)
Palestra: Reliable and Accurate State Estimation for Connected and Autonomous Highway Vehicles
Sumário: Accurate and reliable awareness of world interactions is a key requirement for effective commercial deployment of autonomous and connected vehicles. Awareness arises from onboard sensors and ubiquitous communication between vehicles and infrastructure. Vehicle coordination and safety necessitate reliable “where-in-lane” knowledge of vehicle state. This presentation will address sensor fusion for high-bandwidth vehicle state estimation with a focus on high accuracy and reliability.
Advances in sensing and computation have dramatically altered the focus of related research. For example, computer vision and Global Navigation Satellite Systems each separately provide far more measurements than are necessary for observability. Such environments are signal-rich. The large number of measurements provides both opportunities (e.g., high accuracy) and challenges (e.g., large numbers of outliers). Standard state estimation approaches that decide irrevocably at each time which measurements are valid (e.g. EKF) are not sufficiently reliable at removing the effects of spurious measurements. When that decision is wrong, either measurement information is lost or the state and covariance estimates become corrupted, rendering all subsequent decisions suspect. Either situation can result in divergence of the state estimate, with potentially tragic consequences.
This presentation will consider moving horizon and nonlinear state estimation by a novel risk-averse performance-specified (RAPS) approach. Moving horizon methods extract the Bayesian optimal trajectory using all sensor data over a temporal window (e.g. SLAM and RHE). RAPS modifies the optimization problem to select the least risky set of measurements that satisfies a user-defined performance constraint. RAPS is able to evaluate, and reconsider, outlier assumptions for all measurements within the temporal window. The presentation will include experimental results.
Resumé: Jay A. Farrell (Este endereço de email está sendo protegido de spambots. Você precisa do JavaScript ativado para vê-lo.) received B.S. degrees in physics and electrical engineering from Iowa State University, and M.S. and Ph.D. degrees in electrical engineering from the University of Notre Dame. At Charles Stark Draper Lab (1989–1994), he was the principal investigator on projects involving autonomous vehicles, receiving the Engineering Vice President’s Best Technical Publication Award in 1990 and Recognition Awards for Outstanding Performance and Achievement in 1991 and 1993. He is the KA Endowed Professor in the Department of Electrical and Computer Engineering at the University of California, Riverside, Riverside, CA 92521 USA. For the IEEE Control Systems Society (CSS), he has served as vice president of finance, vice president of technical activities, CSS general vice chair of the 2011 IEEE Conference on Decision and Control (CDC) and European Control Conference, general chair of IEEE CDC 2012, president-elect, president, and past president. For IEEE, he served three terms on the Fellow Committee, as Educational Activities Board treasurer, and on IEEE Financial Committee. In 2020–2021, he served as president of the American Automatic Control Council (AASS). He is the author of more than 250 technical articles and three books. He was recognized as a GNSS Leader to Watch by GPS World Magazine in 2009 and is a Distinguished Member of the CSS, a Fellow of IEEE, the American Association for the Advancement of Science, and the International Federation of Automatic Control.
https://intra.ece.ucr.edu/~
https://scholar.google.com/
Data: 23/10/2023
Horário: 16:00 às 18:00
Local: Salão de Convenções
Inscrições: Gratuitas no SIG